Trade based money laundering (TBML) is no longer just an operational hurdle; it is a complex intelligence challenge. As global trade routes shift and documentation becomes more unstructured, financial institutions must move beyond siloed systems to protect themselves from increasingly sophisticated bad actors.
Mariya George is the CEO and Co-founder of Cleareye.ai. In this discussion with Dipesh Patel of Trade Treasury Payments in Jersey City, she explains why traditional banking silos are failing, the hidden risks in the “slow and small” TBML strategy, and how the combination of LLMs and Agentic AI is revolutionizing compliance.
Dipesh Patel: We are talking about trade based money laundering. Why is it such a persistent challenge for banks?
Mariya George: It is a persistent challenge because historically, banks have treated it as an operational problem, not a lack of intelligence domain problem. Trade finance does not happen in silos. There are multiple documents and jurisdictions involved.
Unfortunately, many banks still have compliance, AML, and operations teams working in isolation. Bad actors know these teams do not talk to each other and they take advantage of those silos to bypass traditional security checks.
Dipesh Patel: ClearEye recently launched a report on the state of trade based money laundering. What stood out most regarding evolving risks?
Mariya George: The most surprising finding was the pace. TBML is not always one huge, suspicious transaction. Instead, it is a set of small things that banks overlook. Because the values are smaller, they do not trigger red flags, but they are happening at a scale that results in a massive problem for the institution.
Dipesh Patel: How do new routes, like the EU-India Free Trade Agreement, present new risks to the market?
Mariya George: When new jurisdictions come in, silos happen more because regulations are changing. The EU-India deal is the mother of all deals, but institutions are not yet ready to apply current regulations to these new structures. What works in the US or UK is very different from the regulatory needs in India. Adapting to these changes is vital to ensure bad actors do not exploit the transition.
Dipesh Patel: Why is unstructured data causing such a compliance blind spot?
Mariya George: Ops teams want efficiency, while Compliance teams want to solve the AML problem. Historically, they have not used the same data effectively.
Now, with Large Language Models (LLMs), we have the ability to understand the intent of the text. For example, in an LC (Letter of Credit), we can analyze human-typed terms and conditions to assess risk and make better recommendations. If both teams adopt this technology, TBML can be reduced significantly.
Dipesh Patel: Is AI a hindrance, a help, or an evolutionary arms race?
Mariya George: Definitely helpful. Think of AI in three buckets:
When you combine a brain that understands intent with hands that can execute, you create a system that can finally close the gaps that money launderers have been using for decades.
To learn more about how Cleareye.ai is using Agentic AI to solve trade based money laundering and operational silos, visit Cleareye.ai.
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